Maintenance practice in industry and the military is typically based on one of two strategies: corrective maintenance and preventative maintenance. The first strategy of corrective maintenance entails repairing or replacing components of a system when they fail or when there is significant performance degradation in the system. Furthermore, parts, supplies, personnel, and tools required for corrective maintenance are often not ready or unavailable, causing repairs to be slow and costly, unless maintenance is scheduled in a timely manner.
To some extent, the second strategy of preventative maintenance attempts to reduce this problem by establishing maintenance schedules based upon statistical analysis—such as mean-time-between-failure, or other criteria. Such scheduled preventative maintenance is an inefficient approach for most systems due to the lack of specific information on the condition of the system or of its components. Schedule intervals are typically shortened to significantly reduce the probability of component failure even under the most adverse operating conditions. Consequently, such maintenance practice is costly because components are replaced before replacement is truly necessary and must be performed more often than would be required if the condition of the system was accurately determined (diagnostic analysis) or if reliable predictions about future faults or failures could be made (prognostic analysis). Furthermore, there is a possibility that a component will fail before its schedule interval expires, resulting in more costly, and possibly disastrous, consequences.
Another strategy is condition-based maintenance whose objective is to carry out replacement or repair when component useful life has been realized and before failure occurs. Condition-based maintenance relies on system monitoring and analysis of the monitored data. Diagnostic techniques for analyzing such monitored data include off-line signal processing (e.g., vibration analysis, parametric modeling), artificial intelligence (e.g., expert systems, model-based reasoning), pattern recognition (e.g., statistical analysis techniques, fuzzy logic, artificial neural networks), and sensor fusion or multisensor integration. The specific diagnostic technique, or combination of techniques, that is selected often depends upon the complexity, and knowledge, of the system and its operating characteristics under normal and abnormal conditions. For example, in those circumstances whereby the monitored data are complex-problems that do not have an algorithmic (or engineering rule) solution or for which the algorithmic solution is too complex to be found, an artificial neural network (ANN) is often implemented.
A valuable capability in condition-based maintenance systems would be to accurately predict when the performance of an operating system will degrade to a level requiring maintenance or when failure will occur. Application of such a predictive maintenance strategy would be especially important for high-value/high-cost systems such as the power trains and mechanical equipment used in civilian/military machinery, including that in land-, air-, and sea-based vehicles (e.g., automobiles, trucks, aircraft, ships, locomotives, and earth-moving equipment). For example, there is a need to apply a predictive maintenance strategy to the AGT 1500 gas turbine engine in the M1 Abrams main battle tank (Greitzer et al, “Gas Turbine Engine Health Monitoring and Prognostics,” presented at the International Society of Logistics 1999 Symposium, Las Vegas, Nev., Aug. 30-Sep. 2, 1999 and Illi et al, “An Artificial Neural Network System for Diagnosing Gas Turbine Engine Fuel Faults,” Advanced Materials and Process Technology for Mechanical Failure Prevention, Proceedings of the 48th Meeting of the Mechanical Failures Prevention Group, April 1994, pp. 359-367). A predictive maintenance strategy would also be beneficial in applications whereby replacement parts are not normally stored on the shelf—that is, in circumstances whereby the replacement parts need a lead time to be ordered, manufactured, or shipped. The strategy also lends itself to preparing maintenance personnel for a pending maintenance task on a certain degrading component or subsystem.
Accordingly, there is a continuing need for a method and apparatus for monitoring, predicting, and/or planning maintenance needs in an operating system.